There is a technology in which the appearance feature amount of a commodity (object) is extracted from the image data of the commodity photographed by an image capturing section and a similarity degree is calculated by comparing the extracted feature amount with the feature amount data of a reference image previously registered in a recognition dictionary file to recognize the category of the commodity according to the calculated similarity degree. Such a technology for recognizing the commodity contained in the image is called as a general object recognition. As to the technology of the general object recognition, various recognition technologies are described in the following document.
Keiji Yanai “Present situation and future of general object recognition”, Journal of Information Processing Society, Vol. 48, No. SIG16 [ Search on Heisei 22 August 10], Internet <URL: http://mm.cs.uec.ac.jp/IPSJ-TCVIM-Yanai.pdf>
In addition, the technology carrying out the general object recognition by performing an area-division on the image for each object is described in the following document.
Jamie Shotton etc., “Semantic Texton Forests for Image Categorization and Segmentation”, [Search on Heisei 22 August 10], Internet <URL: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.14 5.3036&rep=repl&type=pdf>
In recent years, for example, there is a proposal in which the general object recognition technology is applied to a recognition apparatus for recognizing a commodity purchased by a customer, especially, a commodity without a barcode, such as, vegetables, fruits and the like in a checkout system (POS system) of a retail store. However, in a case in which an operator (shop clerk or customer) holds a commodity to be recognized towards an image capturing section, the distance from the image capturing section to the held commodity is not always kept constant. On the other hand, as the number of pixels of the image capturing section is fixed, the resolution of the captured image is varied depending on the distance between the image capturing section and the commodity.
As a result, the similarity degree between the appearance feature amount of the commodity extracted from the captured image and the feature amount data of a reference image is decreased due to the difference in resolution of the captured image and the reference image, which may lead to a low recognition rate.